Reading aloud is not automatic: Processing capacity is required to generate a phonological code from print.
Bibliographic record
Abstract
The present experiments tested the claim that phonological recoding occurs "automatically" by assessing whether it uses central attention in the context of the psychological refractory period paradigm. Task 1 was a tone discrimination task and Task 2 was reading aloud. The joint effects of long-lag word repetition priming and stimulus onset asynchrony (SOA) were underadditive in Experiment 1, suggesting that an early component of lexical processing does not use central attention. In contrast, nonword letter length and grapheme-phoneme complexity yielded additive effects with SOA in Experiments 2, 3, and 4, suggesting that assembled phonology uses central attention. Further, orthographic neighborhood density also yielded additive effects with SOA in Experiments 5, 6, and 7, suggesting that lexical contributions to phonological recoding use central attention. Taken together, the results of these experiments are inconsistent with the widespread claim that phonological codes are assembled and/or addressed automatically. It is suggested that "automaticity" should be replaced by accounts that make more specific claims about how processing unfolds.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".