The reading-attention relationship: Variations in working memory network activity during single word decoding in children with and without dyslexia
Bibliographic record
Abstract
This study utilized a neuroimaging task to assess working memory (WM) network recruitment during single word reading. Associations between WM and reading comprehension skills are well documented. Several converging models suggest WM may also contribute to foundational reading skills, but few studies have assessed this contribution directly. Two groups of children (77 developmental dyslexia (DD), 22 controls) completed a functional magnetic resonance imaging (fMRI) task to identify activation of a priori defined regions of the WM network. fMRI trials consisted of familiar word, pseudoword, and false font stimuli within a 1-back oddball task to assess how activation in the WM network differs in response to stimuli that can respectively be processed using word recognition, phonological decoding, or non-word strategies. Results showed children with DD recruited WM regions bilaterally in response to all stimulus types, whereas control children recruited left-lateralized WM regions during the pseudoword condition only. Group-level comparisons revealed activation differences in the defined WM network regions for false font and familiar word, but not pseudoword conditions. This effect was driven by increased activity in participants with DD in right hemisphere frontal, parietal, and motor regions despite poorer task performance. Findings suggest the WM network may contribute to inefficient decoding and word recognition strategies in children with DD.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".