Letter and grapheme perception in English and Dutch
Bibliographic record
Abstract
In the study of reading, there is a debate about whether letters or graphemes are the primary units of perception. A promising data basis for empirically contributing to this debate can be gained from measuring the perception of single vowel letters compared to vowel digraphs. We used letter detection with masked pseudoword primes on pseudoword targets among skilled native readers in order to test for the existence and time course of vowel digraph effects during reading in deep (English) and shallow (Dutch) orthographies. Selecting these two languages, which are similar in terms of syllabic structure, allowed us to use exactly the same pseudoword stimuli. Results indicate that whereas the Dutch readers show letter effects at short prime durations and digraph effects at longer prime durations, the English readers show only letter effects. These findings are inconsistent with a strong version of the claim that graphemes are perceptual in nature, but consistent with models of reading acquisition and skilled reading that predict that, although letter effects always precede grapheme effects, grapheme activation proceeds faster in relatively shallow orthographies than in relatively deep ones.
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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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".