A reexamination of consonant–vowel differences in masked transposed letter priming effects in the lexical decision task.
Bibliographic record
Abstract
Most orthographic coding models are based on the assumption that the orthographic code does not distinguish between vowels and consonants and, therefore, those models predict no difference between vowel (cisano-CASINO) and consonant (caniso-CASINO) transposed-letter (TL) effects. The available data, however, do provide some evidence for a consonant-vowel distinction at the level of the orthographic code. Most centrally, masked priming lexical decision tasks, mainly carried out in Spanish, have shown priming from consonant TL primes (e.g., caniso) but not from vowel TL primes (e.g., cisano). The present experiments were an investigation of this pattern. Experiment 1, based on Schubert, Kinoshita, and Norris' (2018) stimuli which showed no consonant-vowel differences in an unprimed same-different task, also showed no consonant-vowel differences in masked TL priming effects in lexical decision showing, for the first time, a vowel TL priming effect in that task. Experiment 2, using Lupker, Perea, and Davis' (2008) Experiment 1a stimuli, also showed a small but significant vowel TL priming effect (a nonreplication of that experiment), while replicating the consonant TL priming effect that those authors originally reported. In Experiment 3, TL priming was again essentially unaffected by the consonant-vowel status of the letters involved as well as by target frequency, a variable on which the Experiment 1 and 2 stimuli differed. These results, supported by evoked response potential (ERP) results from other labs, suggest that consonant-vowel TL differences, when they do emerge in English, are likely are not due to the nature of the orthographic code. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".