The locus of serial processing in reading aloud: Orthography–to–phonology computation or speech planning?
Bibliographic record
Abstract
Dual-route theories of reading posit that a sublexical reading mechanism that operates serially and from left to right is involved in the orthography-to-phonology computation. These theories attribute the masked onset priming effect (MOPE) and the phonological Stroop effect (PSE) to the serial left-to-right operation of this mechanism. However, both effects may arise during speech planning, in the phonological encoding process, which also occurs serially and from left to right. In the present paper, we sought to determine the locus of serial processing in reading aloud by testing the contrasting predictions that the dual-route and speech planning accounts make in relation to the MOPE and the PSE. The results from three experiments that used the MOPE and the PSE paradigms in English are inconsistent with the idea that these effects arise during speech planning, and consistent with the claim that a sublexical serially operating reading mechanism is involved in the print-to-sound translation. Simulations of the empirical data on the MOPE with the dual route cascaded (DRC) and connectionist dual process (CDP++) models, which are computational implementations of the dual-route theory of reading, provide further support for the dual-route account.
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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 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".