The proportion of exception words and regular words in a reading list influences reading strategies in behavioural and fMRI data
Bibliographic record
Abstract
There has been considerable functional neuroimaging support for a dual-pathway neuroanatomical model of reading that distinguishes between a ventral whole-word or lexical stream and a dorsal sub-word or sublexical stream. The relative contribution of these two streams while participants read aloud familiar stimuli, however, still remains unclear. This study investigated the relative involvement of the dorsal and ventral streams during reading of highly familiar stimuli by manipulating the proportion of regular words (REGs; stimuli that can be correctly processed by both ventral and dorsal streams) and exception words (EXCs; stimuli that can only be correctly processed by the ventral stream). The behavioural evidence supported modulation of lexical and sublexical pathway contributions. Specifically, when 75% of the words were REGs, both lexical and sublexical information were utilized, as evidenced by the fast reaction times and increased errors for EXCs. In contrast, when 75% of the words were EXCs, participants minimized sublexical processing, as evidenced by fast reaction times and decreased errors for EXCs. Neuroanatomical evidence provided further support, such that reading a REG-predominant list induced recruitment of both ventral and dorsal stream regions, while reading an EXC-predominant list induced recruitment of the ventral stream and the additional employment of a phonological lexical check (via BA6) as response modulation. These results support parallel operation of the dorsal and ventral stream and provide evidence that the extent to which each stream contributes to reading can be modulated
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| 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".