Consonant and vowel transposition effects during reading development: A study on Italian children and adults
Bibliographic record
Abstract
Recently, Colombo, Spinelli, and Lupker, using a masked transposed letter (TL) priming paradigm, investigated whether consonant/vowel (CV) status is important early in orthographic processing. In four experiments with Italian and English adults, they found equivalent TL priming effects for CC, CV, and VC transpositions. Here, we investigated that question with younger readers (aged 7-10) and adults, as well as whether masked TL priming effects might have a phonological basis. That is, because young children are likely to use phonological recoding in reading, the question was whether they would show TL priming that is affected by CV status. In Experiment 1, target words were preceded by primes in which two letters (either CV, VC, or CC) were transposed versus substituted (SL). We found significant TL priming effects, with an increasing developmental trend but, again, no letter type by priming interaction. In Experiment 2, the transpositions/substitutions involved only pairs of vowels with those vowels having either diphthong or hiatus status. The difference between these two types of vowel clusters is only phonological; thus, the question was, "Would TL priming interact with this factor?" TL priming was again found with an increasing trend with age, but there was no vowel cluster by priming interaction. There was, however, an overall vowel cluster effect (slower responding to words with hiatuses) which decreased with age. The results suggest that TL priming only taps the orthographic level, and that CV status only becomes important at a later phonological level.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".