Letter encoding in visual word identification: More evidence for a word-integration explanation for parafoveal preview effects in reading.
Bibliographic record
Abstract
A letter string presented briefly in the parafovea facilitates naming a foveally presented word provided that the two stimuli are orthographically similar. The facilitation (called priming) is asymmetrical in that to obtain it, both letter strings must have the first letters in common. One possible explanation, a letter-integration hypothesis, proposes that readers only identify the letters at the beginning of the parafoveal stimulus, an action that facilitates processing the target. Another explanation, a word-integration hypothesis, postulates that all the letters of the parafoveal stimulus are identified and that the asymmetry occurs because the first letters of the parafoveal stimulus are weighted more heavily than the later ones. The two accounts differ in the way the position of the first letter is determined: The first postulates that readers know the side to identify first without reference to the stimulus; the second postulates that readers establish an order on the stimuli postcategorically. To distinguish the views, we presented English and Hebrew stimuli to bilingual readers. Readers could not anticipate the position of the first letters; hence, if the letter-integration explanation is correct, the asymmetry in the priming should be attenuated. Consistent with the word-integration explanation, however, priming occurred when the target shared the beginning letters with the prime in both languages.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".