Understanding the biological basis of dyslexia at a neural systems level
Why this work is in the frame
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Bibliographic record
Abstract
We examined the naming speed performance of 18 typically achieving and 16 dyslexic adults while simultaneously recording eye movements, articulations and fMRI data. Naming speed tasks, which require participants to name a list of letters or objects, have been proposed as a proxy for reading and are thought to recruit similar reading networks in the left hemisphere of the brain as more complex reading tasks. We employed letter and object naming speed tasks, with task manipulations to make the stimuli more or less phonologically and/or visually similar. Compared to typically achieving readers, readers with dyslexia had a poorer behavioural naming speed task performance, longer fixation durations, more regressions and increased activation in areas of the reading network in the left-hemisphere. Whereas increased network activation was positively associated with performance in dyslexics, it was negatively related to performance in typically achieving readers. Readers with dyslexia had greater bilateral activation and recruited additional regions involved with memory, namely the amygdala and hippocampus; in contrast, the typically achieving readers additionally activated the dorsolateral prefrontal cortex. Areas within the reading network were differentially activated by stimulus manipulations to the naming speed tasks. There was less efficient naming speed behavioural performance, longer fixation durations, more regressions and increased neural activity when letter stimuli were both phonologically and visually similar. Discussion focuses on the differences in activation within the reading network, how they are related to behavioural task differences, and how progress in furthering the understanding of the relationship between behavioural performance and brain activity can change the overall trajectories of children with reading difficulties by contributing to both early identification and remediation processes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it