Orthographic Dependency in the Neural Correlates of Reading: Evidence from Audiovisual Integration in English Readers
Bibliographic record
Abstract
Reading skills are indispensible in modern technological societies. In transparent alphabetic orthographies, such as Dutch, reading skills build on associations between letters and speech sounds (LS pairs). Previously, we showed that the superior temporal cortex (STC) of Dutch readers is sensitive to the congruency of LS pairs. Here, we used functional magnetic resonance imaging to investigate whether a similar congruency sensitivity exists in STC of readers of the more opaque English orthography, where the relation among LS pairs is less reliable. Eighteen subjects passively perceived congruent and incongruent audiovisual pairs of different levels of transparency in English: letters and speech sounds (LS; irregular), letters and letter names (LN; fairly transparent), and numerals and number names (NN; transparent). In STC, we found congruency effects for NN and LN, but no effects in the predicted direction (congruent > incongruent) for LS pairs. These findings contrast with previous results obtained from Dutch readers. These data indicate that, through education, the STC becomes tuned to the congruency of transparent audiovisual pairs, but suggests a different neural processing of irregular mappings. The orthographic dependency of LS integration underscores cross-linguistic differences in the neural basis of reading and potentially has important implications for dyslexia interventions across 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".