What Can Homophone Effects Tell Us About the Nature of Orthographic Representation in Visual Word Recognition
Bibliographic record
Abstract
In a lexical decision task (LDT), Pexman, Lupker, and Jared (2001) reported longer response times for homophones (e.g., MAID-MADE) than for non-homophones (e.g., MESS) and attributed these effects to orthographic competition created by feedback activation from phonology.The focus of the present research was the grain-size of the orthographic units activated by feedback from phonology.We created 9 categories of homophones based on the sublexical, orthographic overlap between members of homophone pairs.We also manipulated the type of foils presented in LDT (consonant strings, pseudowords, pseudohomophones) to create conditions involving less vs. more extensive processing.Homophone effect sizes varied by category; effects were largest when spellings of both onsets and bodies differed within the homophone pairs (e.g., KERNEL-COLONEL) and when members of the homophone pairs differed by vowel graphemes (e.g., BRAKE-BREAK).These results suggest that several specific grain-sizes of orthographic representation are activated by feedback phonology.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".