Examining the Interactivity of Lexical Orthographic and Phonological Processing.
Bibliographic record
Abstract
The number and type of connections involving different levels of orthographic and phonological representations differentiate between several models of spoken and visual word recognition. At the sublexical level of processing, Borowsky, Owen, and Fonos (1999) demonstrated evidence for direct processing connections from grapheme representations to phoneme representations (i.e., a sensitivity effect) over and above any bias effects, but not in the reverse direction. Neural network models of visual word recognition implement an orthography to phonology processing route that involves the same connections for processing sublexical and lexical information, and thus a similar pattern of cross-modal effects for lexical stimuli are expected by models that implement this single type of connection (i.e., orthographic lexical processing should directly affect phonological lexical processing, but not in the reverse direction). Furthermore, several models of spoken word perception predict that there should be no direct connections between orthographic representations and phonological representations, regardless of whether the connections are sublexical or lexical. The present experiments examined these predictions by measuring the influence of a cross-modal word context on word target discrimination. The results provide constraints on the types of connections that can exist between orthographic lexical representations and phonological lexical representations.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".